Description

[U,S,V]
= svd(X) returns numeric unitary matrices U and V with
the columns containing the singular vectors, and a diagonal matrix S containing
the singular values. The matrices satisfy the condition A
= U*S*V', where V' is the Hermitian transpose
(the complex conjugate of the transpose) of V.
The singular vector computation uses variable-precision arithmetic. svd does
not compute symbolic singular vectors. Therefore, the input matrix X must
be convertible to floating-point numbers. For example, it can be a
matrix of symbolic numbers.

[U,S,V]
= svd(X,0) produces the "economy size"
decomposition. If X is an m-by-n matrix
with m > n, then svd computes
only the first n columns of U.
In this case, S is an n-by-n matrix.
For m <= n, this syntax is equivalent to svd(X).

[U,S,V]
= svd(X,'econ') also produces the
"economy size" decomposition. If X is an m-by-n matrix
with m >= n, then this syntax is equivalent
to svd(X,0). For m < n, svd computes
only the first m columns of V.
In this case, S is an m-by-m matrix.

Related Examples

Input Arguments

Input matrix specified as a symbolic matrix. For syntaxes with
one output argument, the elements of X can be symbolic
numbers, variables, expressions, or functions. For syntaxes with three
output arguments, the elements of X must be convertible
to floating-point numbers.